Overview

Dataset statistics

Number of variables14
Number of observations152534
Missing cells506
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.3 MiB
Average record size in memory112.0 B

Variable types

Numeric10
Categorical2
Boolean2

Warnings

Volume_7days is highly correlated with Volume_15daysHigh correlation
Volume_15days is highly correlated with Volume_7days and 1 other fieldsHigh correlation
Volume_Monthly is highly correlated with Volume_15daysHigh correlation
%NFCR is highly correlated with TargetHigh correlation
Target is highly correlated with %NFCRHigh correlation
Volume_7days is highly correlated with Volume_15daysHigh correlation
Volume_15days is highly correlated with Volume_7daysHigh correlation
Volume_Monthly is highly correlated with TargetHigh correlation
Tempo_Medio_Chat is highly correlated with Tempo_Medio_Email and 1 other fieldsHigh correlation
Tempo_Medio_Email is highly correlated with Tempo_Medio_Chat and 1 other fieldsHigh correlation
AWT_Chat is highly correlated with Tempo_Medio_Chat and 1 other fieldsHigh correlation
%NFCR is highly correlated with TargetHigh correlation
Target is highly correlated with Volume_Monthly and 1 other fieldsHigh correlation
Volume_7days is highly correlated with Volume_15daysHigh correlation
Volume_15days is highly correlated with Volume_7daysHigh correlation
Volume_Monthly is highly correlated with TargetHigh correlation
Tempo_Medio_Chat is highly correlated with Tempo_Medio_Email and 1 other fieldsHigh correlation
Tempo_Medio_Email is highly correlated with Tempo_Medio_Chat and 1 other fieldsHigh correlation
AWT_Chat is highly correlated with Tempo_Medio_Chat and 1 other fieldsHigh correlation
%NFCR is highly correlated with TargetHigh correlation
Target is highly correlated with Volume_Monthly and 1 other fieldsHigh correlation
%Insatisfação(CSAT) is highly correlated with CSAT_RatedHigh correlation
%NFCR is highly correlated with TargetHigh correlation
Volume_15days is highly correlated with Volume_7days and 1 other fieldsHigh correlation
Target is highly correlated with %NFCRHigh correlation
Volume_7days is highly correlated with Volume_15days and 1 other fieldsHigh correlation
CSAT_Rated is highly correlated with %Insatisfação(CSAT)High correlation
Volume_Monthly is highly correlated with Volume_15days and 1 other fieldsHigh correlation
Volume_Monthly is highly skewed (γ1 = 23.0229645) Skewed
Volume_7days has 99728 (65.4%) zeros Zeros
Volume_15days has 53598 (35.1%) zeros Zeros
Tempo_Medio_Chat has 57429 (37.6%) zeros Zeros
Tempo_Medio_Email has 86952 (57.0%) zeros Zeros
AWT_Chat has 57075 (37.4%) zeros Zeros
%NFCR has 117210 (76.8%) zeros Zeros
%Insatisfação(CSAT) has 147322 (96.6%) zeros Zeros
CSAT_Rated has 105973 (69.5%) zeros Zeros

Reproduction

Analysis started2021-06-15 18:21:57.238496
Analysis finished2021-06-15 18:22:40.759397
Duration43.52 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Requester_ID
Real number (ℝ≥0)

Distinct128761
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9755498.253
Minimum37
Maximum19086022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:40.872504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile847868.85
Q15357028.5
median9530719.5
Q314923859
95-th percentile18494161
Maximum19086022
Range19085985
Interquartile range (IQR)9566830.5

Descriptive statistics

Standard deviation5580892.875
Coefficient of variation (CV)0.5720766618
Kurtosis-1.221461731
Mean9755498.253
Median Absolute Deviation (MAD)4790297.5
Skewness-0.05654703246
Sum1.488045171 × 1012
Variance3.114636528 × 1013
MonotonicityNot monotonic
2021-06-15T15:22:41.062367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68657224
 
< 0.1%
106084244
 
< 0.1%
169673034
 
< 0.1%
119005954
 
< 0.1%
137190874
 
< 0.1%
113108384
 
< 0.1%
22592564
 
< 0.1%
168135144
 
< 0.1%
140826714
 
< 0.1%
93015474
 
< 0.1%
Other values (128751)152494
> 99.9%
ValueCountFrequency (%)
372
< 0.1%
851
< 0.1%
1111
< 0.1%
2201
< 0.1%
2851
< 0.1%
3942
< 0.1%
6122
< 0.1%
6641
< 0.1%
7591
< 0.1%
8261
< 0.1%
ValueCountFrequency (%)
190860222
< 0.1%
190840581
< 0.1%
190807771
< 0.1%
190807291
< 0.1%
190807021
< 0.1%
190806471
< 0.1%
190767451
< 0.1%
190756731
< 0.1%
190753142
< 0.1%
190751871
< 0.1%

Month
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2021_02
47783 
2021_05
43850 
2021_01
31919 
2021_04
28982 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1067738
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021_01
2nd row2021_01
3rd row2021_01
4th row2021_01
5th row2021_01

Common Values

ValueCountFrequency (%)
2021_0247783
31.3%
2021_0543850
28.7%
2021_0131919
20.9%
2021_0428982
19.0%

Length

2021-06-15T15:22:41.427516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T15:22:41.523335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2021_0247783
31.3%
2021_0543850
28.7%
2021_0131919
20.9%
2021_0428982
19.0%

Most occurring characters

ValueCountFrequency (%)
2352851
33.0%
0305068
28.6%
1184453
17.3%
_152534
14.3%
543850
 
4.1%
428982
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number915204
85.7%
Connector Punctuation152534
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2352851
38.6%
0305068
33.3%
1184453
20.2%
543850
 
4.8%
428982
 
3.2%
Connector Punctuation
ValueCountFrequency (%)
_152534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1067738
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2352851
33.0%
0305068
28.6%
1184453
17.3%
_152534
14.3%
543850
 
4.1%
428982
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1067738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2352851
33.0%
0305068
28.6%
1184453
17.3%
_152534
14.3%
543850
 
4.1%
428982
 
2.7%

Volume_7days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4351816644
Minimum0
Maximum43
Zeros99728
Zeros (%)65.4%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:41.648651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum43
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7234225162
Coefficient of variation (CV)1.662346039
Kurtosis137.5918115
Mean0.4351816644
Median Absolute Deviation (MAD)0
Skewness4.667368484
Sum66380
Variance0.523340137
MonotonicityNot monotonic
2021-06-15T15:22:41.766558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
099728
65.4%
143016
28.2%
27315
 
4.8%
31717
 
1.1%
4496
 
0.3%
5156
 
0.1%
667
 
< 0.1%
714
 
< 0.1%
812
 
< 0.1%
94
 
< 0.1%
Other values (8)9
 
< 0.1%
ValueCountFrequency (%)
099728
65.4%
143016
28.2%
27315
 
4.8%
31717
 
1.1%
4496
 
0.3%
5156
 
0.1%
667
 
< 0.1%
714
 
< 0.1%
812
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
431
 
< 0.1%
361
 
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
181
 
< 0.1%
141
 
< 0.1%
131
 
< 0.1%
102
 
< 0.1%
94
 
< 0.1%
812
< 0.1%

Volume_15days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8544193426
Minimum0
Maximum83
Zeros53598
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:41.896321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum83
Range83
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9083294299
Coefficient of variation (CV)1.063095584
Kurtosis506.5507973
Mean0.8544193426
Median Absolute Deviation (MAD)0
Skewness8.050614037
Sum130328
Variance0.8250623532
MonotonicityNot monotonic
2021-06-15T15:22:42.017049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
177608
50.9%
053598
35.1%
215150
 
9.9%
34057
 
2.7%
41275
 
0.8%
5470
 
0.3%
6204
 
0.1%
779
 
0.1%
843
 
< 0.1%
918
 
< 0.1%
Other values (12)32
 
< 0.1%
ValueCountFrequency (%)
053598
35.1%
177608
50.9%
215150
 
9.9%
34057
 
2.7%
41275
 
0.8%
5470
 
0.3%
6204
 
0.1%
779
 
0.1%
843
 
< 0.1%
918
 
< 0.1%
ValueCountFrequency (%)
831
< 0.1%
431
< 0.1%
411
< 0.1%
221
< 0.1%
211
< 0.1%
171
< 0.1%
161
< 0.1%
151
< 0.1%
132
< 0.1%
122
< 0.1%

Volume_Monthly
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.369255379
Minimum0
Maximum123
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:42.388556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum123
Range123
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9331045571
Coefficient of variation (CV)0.6814686079
Kurtosis2303.634694
Mean1.369255379
Median Absolute Deviation (MAD)0
Skewness23.0229645
Sum208858
Variance0.8706841144
MonotonicityNot monotonic
2021-06-15T15:22:42.517534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1115770
75.9%
225301
 
16.6%
37177
 
4.7%
42415
 
1.6%
5921
 
0.6%
6450
 
0.3%
7197
 
0.1%
8112
 
0.1%
058
 
< 0.1%
946
 
< 0.1%
Other values (14)87
 
0.1%
ValueCountFrequency (%)
058
 
< 0.1%
1115770
75.9%
225301
 
16.6%
37177
 
4.7%
42415
 
1.6%
5921
 
0.6%
6450
 
0.3%
7197
 
0.1%
8112
 
0.1%
946
 
< 0.1%
ValueCountFrequency (%)
1231
 
< 0.1%
731
 
< 0.1%
661
 
< 0.1%
351
 
< 0.1%
211
 
< 0.1%
202
 
< 0.1%
194
< 0.1%
181
 
< 0.1%
161
 
< 0.1%
147
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
False
152131 
True
 
403
ValueCountFrequency (%)
False152131
99.7%
True403
 
0.3%
2021-06-15T15:22:42.620589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size149.1 KiB
False
152267 
True
 
267
ValueCountFrequency (%)
False152267
99.8%
True267
 
0.2%
2021-06-15T15:22:42.676447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Tempo_Medio_Chat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct93722
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean727.1248672
Minimum0
Maximum12454.309
Zeros57429
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:42.775414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median575.604
Q31148.8005
95-th percentile2278.01705
Maximum12454.309
Range12454.309
Interquartile range (IQR)1148.8005

Descriptive statistics

Standard deviation827.4414861
Coefficient of variation (CV)1.1379634
Kurtosis5.469969156
Mean727.1248672
Median Absolute Deviation (MAD)575.604
Skewness1.690214618
Sum110911264.5
Variance684659.4129
MonotonicityNot monotonic
2021-06-15T15:22:42.935084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057429
37.6%
450.9973
 
< 0.1%
1504.5163
 
< 0.1%
2348.4923
 
< 0.1%
848.14899993
 
< 0.1%
790.32800013
 
< 0.1%
573.23900013
 
< 0.1%
490.78200013
 
< 0.1%
685.0593
 
< 0.1%
650.1413
 
< 0.1%
Other values (93712)95078
62.3%
ValueCountFrequency (%)
057429
37.6%
4.1140000821
 
< 0.1%
4.4269998071
 
< 0.1%
7.7030000691
 
< 0.1%
8.331000091
 
< 0.1%
8.4519999031
 
< 0.1%
8.6240000721
 
< 0.1%
8.8180000781
 
< 0.1%
8.901999951
 
< 0.1%
9.2109999661
 
< 0.1%
ValueCountFrequency (%)
12454.3091
< 0.1%
11140.8421
< 0.1%
10618.6991
< 0.1%
10169.4271
< 0.1%
9502.5831
< 0.1%
9328.7581
< 0.1%
9049.8611
< 0.1%
8783.7421
< 0.1%
8768.0811
< 0.1%
8716.9331
< 0.1%

Tempo_Medio_Email
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct65071
Distinct (%)42.8%
Missing506
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean4527.370896
Minimum0
Maximum352818.3333
Zeros86952
Zeros (%)57.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:43.098339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36497.21875
95-th percentile19677.13958
Maximum352818.3333
Range352818.3333
Interquartile range (IQR)6497.21875

Descriptive statistics

Standard deviation9511.600142
Coefficient of variation (CV)2.100910299
Kurtosis157.5505026
Mean4527.370896
Median Absolute Deviation (MAD)0
Skewness8.339119467
Sum688287142.6
Variance90470537.26
MonotonicityNot monotonic
2021-06-15T15:22:43.256243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086952
57.0%
12917.708332
 
< 0.1%
9012.8333332
 
< 0.1%
3694.3333332
 
< 0.1%
2540.5833332
 
< 0.1%
21688.583332
 
< 0.1%
8097.0416672
 
< 0.1%
8110.6666671
 
< 0.1%
7511.3751
 
< 0.1%
6579.5833331
 
< 0.1%
Other values (65061)65061
42.7%
(Missing)506
 
0.3%
ValueCountFrequency (%)
086952
57.0%
12.833333331
 
< 0.1%
16.333333321
 
< 0.1%
20.041666681
 
< 0.1%
27.583333341
 
< 0.1%
29.333333341
 
< 0.1%
301
 
< 0.1%
34.541666661
 
< 0.1%
38.791666661
 
< 0.1%
58.291666661
 
< 0.1%
ValueCountFrequency (%)
352818.33331
< 0.1%
317561.66671
< 0.1%
314201.751
< 0.1%
306671.251
< 0.1%
297123.83331
< 0.1%
281697.8751
< 0.1%
278101.70831
< 0.1%
276913.51
< 0.1%
276580.41671
< 0.1%
271761.04171
< 0.1%

AWT_Chat
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5475
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.00344762
Minimum0
Maximum3964.471
Zeros57075
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:43.534753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q331.5
95-th percentile407
Maximum3964.471
Range3964.471
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation227.0716534
Coefficient of variation (CV)3.110423696
Kurtosis37.72168388
Mean73.00344762
Median Absolute Deviation (MAD)10
Skewness5.537384326
Sum11135507.88
Variance51561.53578
MonotonicityNot monotonic
2021-06-15T15:22:43.797072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
057075
37.4%
83960
 
2.6%
93914
 
2.6%
103440
 
2.3%
73424
 
2.2%
113052
 
2.0%
122833
 
1.9%
132408
 
1.6%
62384
 
1.6%
142107
 
1.4%
Other values (5465)67937
44.5%
ValueCountFrequency (%)
057075
37.4%
0.41700005531
 
< 0.1%
23
 
< 0.1%
2.2779998781
 
< 0.1%
329
 
< 0.1%
3.1010000711
 
< 0.1%
3.4149999621
 
< 0.1%
3.8663333261
 
< 0.1%
4376
 
0.2%
4.0130000111
 
< 0.1%
ValueCountFrequency (%)
3964.4711
< 0.1%
3799.1081
< 0.1%
34291
< 0.1%
33261
< 0.1%
33231
< 0.1%
33121
< 0.1%
32551
< 0.1%
32051
< 0.1%
31961
< 0.1%
31731
< 0.1%

%NFCR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.160587886
Minimum0
Maximum1
Zeros117210
Zeros (%)76.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:44.083323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3194564512
Coefficient of variation (CV)1.989293584
Kurtosis1.757543736
Mean0.160587886
Median Absolute Deviation (MAD)0
Skewness1.794806351
Sum24495.1126
Variance0.1020524242
MonotonicityNot monotonic
2021-06-15T15:22:44.351243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0117210
76.8%
114142
 
9.3%
0.512790
 
8.4%
0.33333333333472
 
2.3%
0.66666666672201
 
1.4%
0.25814
 
0.5%
0.75511
 
0.3%
0.6327
 
0.2%
0.4309
 
0.2%
0.2154
 
0.1%
Other values (39)604
 
0.4%
ValueCountFrequency (%)
0117210
76.8%
0.1251
 
< 0.1%
0.14285714297
 
< 0.1%
0.166666666728
 
< 0.1%
0.2154
 
0.1%
0.25814
 
0.5%
0.285714285723
 
< 0.1%
0.33333333333472
 
2.3%
0.37512
 
< 0.1%
0.4309
 
0.2%
ValueCountFrequency (%)
114142
9.3%
0.92857142861
 
< 0.1%
0.92307692311
 
< 0.1%
0.91666666671
 
< 0.1%
0.90909090913
 
< 0.1%
0.95
 
< 0.1%
0.88888888893
 
< 0.1%
0.87512
 
< 0.1%
0.857142857133
 
< 0.1%
0.84210526321
 
< 0.1%

%Insatisfação(CSAT)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03116082524
Minimum0
Maximum1
Zeros147322
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:44.583664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1697143173
Coefficient of variation (CV)5.446399959
Kurtosis27.521689
Mean0.03116082524
Median Absolute Deviation (MAD)0
Skewness5.397595305
Sum4753.085317
Variance0.02880294951
MonotonicityNot monotonic
2021-06-15T15:22:44.798485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0147322
96.6%
14335
 
2.8%
0.5665
 
0.4%
0.3333333333120
 
0.1%
0.666666666747
 
< 0.1%
0.2520
 
< 0.1%
0.27
 
< 0.1%
0.46
 
< 0.1%
0.82
 
< 0.1%
0.14285714292
 
< 0.1%
Other values (7)8
 
< 0.1%
ValueCountFrequency (%)
0147322
96.6%
0.11111111111
 
< 0.1%
0.1251
 
< 0.1%
0.14285714292
 
< 0.1%
0.16666666671
 
< 0.1%
0.27
 
< 0.1%
0.2520
 
< 0.1%
0.28571428571
 
< 0.1%
0.3333333333120
 
0.1%
0.46
 
< 0.1%
ValueCountFrequency (%)
14335
2.8%
0.82
 
< 0.1%
0.77777777781
 
< 0.1%
0.752
 
< 0.1%
0.666666666747
 
< 0.1%
0.61
 
< 0.1%
0.5665
 
0.4%
0.46
 
< 0.1%
0.3333333333120
 
0.1%
0.28571428571
 
< 0.1%

CSAT_Rated
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.341137058
Minimum0
Maximum10
Zeros105973
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2021-06-15T15:22:45.014298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5624506892
Coefficient of variation (CV)1.648752828
Kurtosis7.601910132
Mean0.341137058
Median Absolute Deviation (MAD)0
Skewness1.938748744
Sum52035
Variance0.3163507778
MonotonicityNot monotonic
2021-06-15T15:22:45.206667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0105973
69.5%
142117
 
27.6%
23694
 
2.4%
3577
 
0.4%
4117
 
0.1%
532
 
< 0.1%
79
 
< 0.1%
68
 
< 0.1%
84
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
0105973
69.5%
142117
 
27.6%
23694
 
2.4%
3577
 
0.4%
4117
 
0.1%
532
 
< 0.1%
68
 
< 0.1%
79
 
< 0.1%
84
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
92
 
< 0.1%
84
 
< 0.1%
79
 
< 0.1%
68
 
< 0.1%
532
 
< 0.1%
4117
 
0.1%
3577
 
0.4%
23694
 
2.4%
142117
27.6%

Target
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
112602 
1
39932 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters152534
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0112602
73.8%
139932
 
26.2%

Length

2021-06-15T15:22:45.654756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-15T15:22:45.797590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0112602
73.8%
139932
 
26.2%

Most occurring characters

ValueCountFrequency (%)
0112602
73.8%
139932
 
26.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number152534
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112602
73.8%
139932
 
26.2%

Most occurring scripts

ValueCountFrequency (%)
Common152534
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112602
73.8%
139932
 
26.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII152534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112602
73.8%
139932
 
26.2%

Interactions

2021-06-15T15:22:15.158416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:15.445921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:15.908662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:16.134855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:16.373290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:16.687854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:16.934773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:17.177917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:17.387511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:17.601328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:17.818589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:18.117477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:18.333370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:18.528536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:18.727993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:18.942005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:19.161570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:19.379751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:19.666521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:19.909307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:20.164747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:20.450021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:20.689752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:20.927609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:21.143297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:21.345390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:21.549539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:21.749575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:21.923320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:22.113270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:22.418333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:22.659557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:22.852481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:23.042310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:23.361301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:23.574832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:23.785711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:23.998523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:24.232348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:24.466367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:24.690325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:24.924694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:25.141117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:25.364809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:25.598311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:25.847955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:26.091037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:26.348638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:26.554857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:26.901128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:27.239638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:27.653614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:27.931087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:28.464500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:28.851674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:29.597871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:29.858795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:30.211162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:30.467862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:30.726124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:31.023096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:31.336404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:31.584601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:31.816791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:32.045654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:32.282798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:32.529795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:32.774382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:32.989189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:33.211323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:33.445356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:33.662613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:33.850397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:34.032342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:34.235260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:34.435855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:34.647002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:34.857401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:35.063543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:35.281756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:35.491497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:35.710248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:35.899833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:36.113473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:36.317505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:36.521448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:36.727794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:36.927927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:37.116350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:37.316884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:37.506763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:37.719320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:37.901051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:38.089678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:38.288566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:38.490512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:38.698267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:38.911131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:39.101325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-15T15:22:39.291361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-15T15:22:45.942794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-15T15:22:46.230876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-15T15:22:46.508480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-15T15:22:46.783947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-15T15:22:47.054461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-15T15:22:39.565482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-15T15:22:40.063705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-06-15T15:22:40.513431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Requester_IDMonthVolume_7daysVolume_15daysVolume_MonthlyReclameAquiSocialMediaTempo_Medio_ChatTempo_Medio_EmailAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_RatedTarget
0100056932021_01111nono0.000008795.6666670.000.0000000.000
1100080912021_01111nono0.000002503.7500000.000.0000000.000
2100145132021_01111nono0.0000012367.2083330.000.0000000.000
3100148832021_01122nono1301.671008811.29166710.000.6666670.001
4100151422021_01012nono409.1980013107.41666745.000.5000000.001
5100163412021_01455nono1305.762757878.16666720.251.0000000.001
6100164212021_01002noyes0.000005461.4166670.000.5000000.001
710018382021_01002noyes0.000005463.3750000.000.5000000.001
8100201182021_01222noyes2929.0850025524.25000036.000.0000000.521
9100217332021_01112noyes0.000005051.0416670.000.5000000.001

Last rows

Requester_IDMonthVolume_7daysVolume_15daysVolume_MonthlyReclameAquiSocialMediaTempo_Medio_ChatTempo_Medio_EmailAWT_Chat%NFCR%Insatisfação(CSAT)CSAT_RatedTarget
15252499895422021_05002nono1315.27250.010.50.00.020
15252599943252021_05001nono1599.98300.0708.00.00.010
15252699949182021_05111nono1397.19300.01073.00.00.000
15252799952032021_05001nono1233.62900.044.00.00.000
15252899958862021_05001nono219.47200.04.00.00.000
1525299996772021_05001nono895.51500.07.00.00.000
15253099968592021_05001nono221.70700.013.00.00.010
15253199969792021_05001nono258.60500.020.00.00.010
15253299972021_05001nono2068.57100.043.00.00.000
15253399975152021_05111nono3276.51700.08.00.00.000